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Surveying the Landscape: Compound Methods for Aspect-Based Sentiment Analysis

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Databases Theory and Applications (ADC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14386))

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Abstract

Aspect-based sentiment Analysis (ABSA) has emerged as a critical research area in natural language processing, facilitating a deeper understanding of user opinions and sentiments expressed in text. This review article comprehensively surveys the landscape of deep learning approaches in ABSA, focusing on triplet and quadruplet ABSA. We delve into the significance of ABSA in diverse domains and present an overview of the critical components and challenges associated with compound ABSA tasks. The review analyzes state-of-the-art models, encompassing pipeline-based and generative-based solutions. Comparative analysis demonstrates the advantages and limitations of these approaches, including their performance, generalizability, and efficiency. Additionally, we explore the domains and datasets used in ABSA research and highlight the crucial factors contributing to ABSA task solutions’ effectiveness. This comprehensive review highlights current challenges and fosters further advancements in compound ABSA tasks.

We would like to express our sincere gratitude to the Saudi Arabian Cultural Mission in Australia and the Ministry of Education (Saudi Arabia) for their unwavering support and financial assistance, which made this work possible.

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References

  1. Bao, X., Zhongqing, W., Jiang, X., Xiao, R., Li, S.: Aspect-based sentiment analysis with opinion tree generation. In: Raedt, L.D. (ed.) Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-2022, pp. 4044–4050. International Joint Conferences on Artificial Intelligence Organization (2022). Main Track

    Google Scholar 

  2. Bhamare, B.R., Jeyanthi, P., Subhashini, R.: Aspect level sentiment analysis approaches. In: 2019 5th International Conference on Computing, Communication, Control and Automation (ICCUBEA), pp. 1–5 (2019)

    Google Scholar 

  3. Bi, S., Li, Z., Brown, M., Wang, L., Xu, Y.: Dynamic weighted and heat-map integrated scalable information path-planning algorithm. EAI Endorsed Trans. Scalable Inf. Syst. 10(2), 1–11 (2022). https://eudl.eu/pdf/10.4108/eetsis.v9i5.1567

  4. Birjali, M., Kasri, M., Beni-Hssane, A.: A comprehensive survey on sentiment analysis: approaches, challenges and trends. Knowl.-Based Syst. 226, 107134 (2021)

    Article  Google Scholar 

  5. Brauwers, G., Frasincar, F.: A survey on aspect-based sentiment classification. ACM Comput. Surv. 55(4), 1–37 (2022)

    Article  Google Scholar 

  6. Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, Barcelona, Spain, pp. 833–843. International Committee on Computational Linguistics (2020)

    Google Scholar 

  7. Cai, H., Xia, R., Yu, J.: Aspect-category-opinion-sentiment quadruple extraction with implicit aspects and opinions. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 340–350 (2021)

    Google Scholar 

  8. Chebolu, S.U.S., Dernoncourt, F., Lipka, N., Solorio, T.: Survey of aspect-based sentiment analysis datasets. arXiv preprint arXiv:2204.05232 (2022)

  9. Chen, Y., Han, S., Chen, G., Yin, J., Wang, K.N., Cao, J.: A deep reinforcement learning-based wireless body area network offloading optimization strategy for healthcare services. Health Inf. Sci. Syst. 11(1), 8 (2023). https://doi.org/10.1007/s13755-023-00212-3

    Article  Google Scholar 

  10. Do, H.H., Prasad, P.W., Maag, A., Alsadoon, A.: Deep learning for aspect-based sentiment analysis: a comparative review. Expert Syst. Appl. 118, 272–299 (2019)

    Article  Google Scholar 

  11. Gao, T., et al.: LEGO-ABSA: a prompt-based task assemblable unified generative framework for multi-task aspect-based sentiment analysis. In: Proceedings of the 29th International Conference on Computational Linguistics, pp. 7002–7012 (2022)

    Google Scholar 

  12. Hong, W., et al.: Graph intelligence enhanced bi-channel insider threat detection. In: Yuan, X., Bai, G., Alcaraz, C., Majumdar, S. (eds.) NSS 2022. LNCS, vol. 13787, pp. 86–102. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-23020-2_5

    Chapter  Google Scholar 

  13. Hong, W., et al.: A graph empowered insider threat detection framework based on daily activities. ISA Trans. 141, 84–92 (2023)

    Article  Google Scholar 

  14. Jiang, Q., Chen, L., Xu, R., Ao, X., Yang, M.: A challenge dataset and effective models for aspect-based sentiment analysis. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 6280–6285 (2019)

    Google Scholar 

  15. Jing, H., Li, Z., Zhao, H., Jiang, S.: Seeking common but distinguishing difference, a joint aspect-based sentiment analysis model. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Punta Cana, Dominican Republic, pp. 3910–3922. Association for Computational Linguistics (2021)

    Google Scholar 

  16. Li, Y., Wang, F., Zhong, S.H.: A more fine-grained aspect-sentiment-opinion triplet extraction task. Mathematics 11(14), 3165 (2023)

    Article  Google Scholar 

  17. Li, Z., Zou, Y., Zhang, C., Zhang, Q., Wei, Z.: Learning implicit sentiment in aspect-based sentiment analysis with supervised contrastive pre-training. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Punta Cana, Dominican Republic, pp. 246–256. Association for Computational Linguistics (2021)

    Google Scholar 

  18. Liu, B.: Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press, Cambridge (2015)

    Book  Google Scholar 

  19. Lu, W., Stepchenkova, S.: User-generated content as a research mode in tourism and hospitality applications: topics, methods, and software. J. Hospitality Mark. Manag. 24(2), 119–154 (2015)

    Article  Google Scholar 

  20. Mao, Y., Shen, Y., Yang, J., Zhu, X., Cai, L.: Seq2Path: generating sentiment tuples as paths of a tree. In: Findings of the Association for Computational Linguistics: ACL 2022, Dublin, Ireland, pp. 2215–2225. Association for Computational Linguistics (2022)

    Google Scholar 

  21. Peng, H., Xu, L., Bing, L., Huang, F., Lu, W., Si, L.: Knowing what, how and why: a near complete solution for aspect-based sentiment analysis. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 8600–8607 (2020)

    Google Scholar 

  22. Peper, J., Wang, L.: Generative aspect-based sentiment analysis with contrastive learning and expressive structure. In: Findings of the Association for Computational Linguistics: EMNLP 2022, Abu Dhabi, United Arab Emirates, pp. 6089–6095. Association for Computational Linguistics (2022)

    Google Scholar 

  23. Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., Androutsopoulos, I.: SemEval-2015 task 12: aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495 (2015)

    Google Scholar 

  24. Pontiki, M., et al.: SemEval-2016 task 5: aspect based sentiment analysis. In: ProWorkshop on Semantic Evaluation (SemEval-2016), pp. 19–30. Association for Computational Linguistics (2016)

    Google Scholar 

  25. Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., Manandhar, S.: SemEval-2014 task 4: aspect based sentiment analysis. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), Dublin, Ireland, pp. 27–35. Association for Computational Linguistics (2014)

    Google Scholar 

  26. Girija, V.R., Sudha, T.: A comparative review on approaches of aspect level sentiment analysis. In: 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS), pp. 770–777 (2023)

    Google Scholar 

  27. Sarki, R., Ahmed, K., Wang, H., Zhang, Y., Wang, K.: Convolutional neural network for multi-class classification of diabetic eye disease. EAI Endorsed Trans. Scalable Inf. Syst. 9(4), e5–e5 (2022)

    Google Scholar 

  28. Shah, R., Zimmermann, R.: Multimodal Analysis of User-Generated Multimedia Content. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61807-4

    Book  Google Scholar 

  29. Shaodong, H., Yingqun, C., Guihong, C., Yin, J., Wang, H., Cao, J.: Multi-step reinforcement learning-based offloading for vehicle edge computing. In: 2023 15th International Conference on Advanced Computational Intelligence (ICACI), pp. 1–8. IEEE (2023)

    Google Scholar 

  30. Singh, R., et al.: Antisocial behavior identification from twitter feeds using traditional machine learning algorithms and deep learning. EAI Endorsed Trans. Scalable Inf. Syst. 10(4), e17–e17 (2023)

    Article  Google Scholar 

  31. Soni, P.K., Rambola, R.: A survey on implicit aspect detection for sentiment analysis: terminology, issues, and scope. IEEE Access 10, 63932–63957 (2022)

    Article  Google Scholar 

  32. Tang, C., Cheng, Y., Yin, J.: An optimized algorithm of grid calibration in WSN node deployment based on the energy consumption distribution model. J. Inf. Comput. Sci. 9(4), 1035–1042 (2012)

    Google Scholar 

  33. Timoshenko, A., Hauser, J.R.: Identifying customer needs from user-generated content. Mark. Sci. 38(1), 1–20 (2019)

    Article  Google Scholar 

  34. Truşcǎ, M.M., Frasincar, F.: Survey on aspect detection for aspect-based sentiment analysis. Artif. Intell. Rev. 56(5), 3797–3846 (2023). https://doi.org/10.1007/s10462-022-10252-y

    Article  Google Scholar 

  35. Wang, J., et al.: Aspect sentiment classification towards question-answering with reinforced bidirectional attention network. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3548–3557 (2019)

    Google Scholar 

  36. Yan, H., Dai, J., Ji, T., Qiu, X., Zhang, Z.: A unified generative framework for aspect-based sentiment analysis. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 2416–2429. Association for Computational Linguistics (2021)

    Google Scholar 

  37. Yin, J., You, M., Cao, J., Wang, H., Tang, M.J., Ge, Y.-F.: Data-driven hierarchical neural network modeling for high-pressure feedwater heater group. In: Borovica-Gajic, R., Qi, J., Wang, W. (eds.) ADC 2020. LNCS, vol. 12008, pp. 225–233. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39469-1_19

    Chapter  Google Scholar 

  38. Zhang, W., Deng, Y., Li, X., Yuan, Y., Bing, L., Lam, W.: Aspect sentiment quad prediction as paraphrase generation. arXiv preprint arXiv:2110.00796 (2021)

  39. Zhang, W., Li, X., Deng, Y., Bing, L., Lam, W.: Towards generative aspect-based sentiment analysis. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pp. 504–510 (2021)

    Google Scholar 

  40. Zhang, W., Li, X., Deng, Y., Bing, L., Lam, W.: A survey on aspect-based sentiment analysis: tasks, methods, and challenges. IEEE Trans. Knowl. Data Eng. 35(11), 11019–11038 (2022)

    Article  Google Scholar 

  41. Zhou, J., Yang, H., He, Y., Mou, H., Yang, J.: A unified one-step solution for aspect sentiment quad prediction. In: Findings of the Association for Computational Linguistics: ACL 2023, Toronto, Canada, pp. 12249–12265. Association for Computational Linguistics (2023)

    Google Scholar 

  42. Zhou, Y., Lin, Z., La, Y., Huang, J., Wang, X.: Analysis and design of power system transformer standard based on knowledge graph. EAI Endorsed Trans. Scalable Inf. Syst. 10(2), 1–8 (2022). https://eudl.eu/pdf/10.4108/eetsis.v9i6.2642

  43. Zhu, L., Xu, M., Bao, Y., Xu, Y., Kong, X.: Deep learning for aspect-based sentiment analysis: a review. PeerJ Comput. Sci. 8, e1044 (2022)

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, VIC, 3011, Australia

    Marwah Alharbi, Jiao Yin & Hua Wang

Authors
  1. Marwah Alharbi
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  2. Jiao Yin
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  3. Hua Wang
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Corresponding author

Correspondence to Jiao Yin .

Editor information

Editors and Affiliations

  1. Royal Melbourne Institute of Technology, Melbourne, VIC, Australia

    Zhifeng Bao

  2. The University of Melbourne, Melbourne, VIC, Australia

    Renata Borovica-Gajic

  3. The University of Queensland, Brisbane, QLD, Australia

    Ruihong Qiu

  4. The University of Melbourne, Melbourne, VIC, Australia

    Farhana Choudhury

  5. The University of New South Wales, Sydney, NSW, Australia

    Zhengyi Yang

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Alharbi, M., Yin, J., Wang, H. (2024). Surveying the Landscape: Compound Methods for Aspect-Based Sentiment Analysis. In: Bao, Z., Borovica-Gajic, R., Qiu, R., Choudhury, F., Yang, Z. (eds) Databases Theory and Applications. ADC 2023. Lecture Notes in Computer Science, vol 14386. Springer, Cham. https://doi.org/10.1007/978-3-031-47843-7_8

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  • DOI: https://doi.org/10.1007/978-3-031-47843-7_8

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